DOI: https://doi.org/10.14710/geoplanning.4.2.157-170

THE PERFORMANCE OF LAND USE CHANGE CAUSATIVE FACTOR ON LANDSLIDE SUSCEPTIBILITY MAP IN UPPER UJUNG-LOE WATERSHEDS SOUTH SULAWESI, INDONESIA

*Andang Suryana Soma orcid scopus  -  Graduate School of Bio-Resources and Environmental Science, Kyushu University, Japan
Tetsuya Kubota scopus  -  Kyushu University, Japan

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Abstract
The study aims to develop and apply land use change (LUC) performance on landslide susceptibility map using frequency ratio (FR), and Logistic regression (LR) method in a geographic information system. In the study area, Upper Ujung-loe Watersheds area of Indonesia, landslides were detected using field survey and air photography from time series data image of Google Earth Pro from 2012 to 2016 and LUC from 2004 to 2011. Landslide susceptibility map (LSM) was constructed using FR and LR with nine causative factors. The result indicated that LUC affect the production of LSM. Validation of landslide susceptibility was carried out in this study at both with and without LUC causative factors. First, performances of each landslide model were tested using AUC curve for success and predictive rate. The highest value of predictive rate at with LUC in both FR and LR method were 83.4 % and 85.2 %, respectively. In the second stage, the ratio of landslides falling on high to a very high class of susceptibility was obtained, which indicates the level of accuracy of the method.LR method with LUC had the highest accuracy of 80.24 %. Taken together, the results suggested that changing the vegetation to another landscape causes slopes unstable and increases probability to landslide occurrence.
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Keywords: Land use change, landslide susceptibility, frequency ratio, logistic regression
Funding: BPPLN DIKTI 2015, Hasanuddin University, Kyushu University

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Section: Original Research
Language : EN
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  1. Akgun, A., Sezer, E. A., Nefeslioglu, H. A., Gokceoglu, C., & Pradhan, B. (2012). An easy-to-use MATLAB program (MamLand) for the assessment of landslide susceptibility using a Mamdani fuzzy algorithm. Computers and Geosciences, 38(1), 23–34. [https://doi.org/10.1016/j.cageo.2011.04.012">Crossref]

  2. Ayalew, L., & Yamagishi, H. (2005). The application of GIS-based logistic regression for landslide susceptibility mapping in the Kakuda-Yahiko Mountains , Central Japan, 65, 15–31. [https://doi.org/10.1016/j.geomorph.2004.06.010">Crossref]

  3. Bai, S. B., Wang, J., Lü, G. N., Zhou, P. G., Hou, S. S., & Xu, S. N. (2010). GIS-based logistic regression for landslide susceptibility mapping of the Zhongxian segment in the Three Gorges area, China. Geomorphology, 115(1–2), 23–31. [https://doi.org/10.1016/j.geomorph.2009.09.025">Crossref]  

  4. Can, T., Nefeslioglu, H. A., Gokceoglu, C., Sonmez, H., & Duman, T. Y. (2005). Susceptibility assessments of shallow earthflows triggered by heavy rainfall at three catchments by logistic regression analyses. Geomorphology, 72(1), 250–271. [https://doi.org/http:/dx.doi.org/10.1016/j.geomorph.2005.05.011">Crossref]  

  5. Chau, K. T., & Chan, J. E. (2005). Regional bias of landslide data in generating susceptibility maps using logistic regression: Case of Hong Kong Island. Landslides, 2(4), 280–290. [https://doi.org/10.1007/s10346-005-0024-x">Crossref]

  6. Chauhan, S., Sharma, M., Arora, M. K., & Gupta, N. K. (2010). Landslide susceptibility zonation through ratings derived from artificial neural network. International Journal of Applied Earth Observation and Geoinformation, 12(5), 340–350. [https://doi.org/10.1016/j.jag.2010.04.006">Crossref]  

  7. Chung, C.-J. F., & Fabbri, A. G. (2003). Validation of Spatial Prediction Models for Landslide Hazard Mapping. Natural Hazards, 30(3), 451–472. [https://doi.org/10.1023/B:NHAZ.0000007172.62651.2b">Crossref]  

  8. Dai, F. C., Lee, C. F., Tham, L. G., Ng, K. C., & Shum, W. L. (2004). Logistic regression modelling of storm-induced shallow landsliding in time and space on natural terrain of Lantau Island, Hong Kong. Bulletin of Engineering Geology and the Environment, 63(4), 315–327. [https://doi.org/10.1007/s10064-004-0245-6">Crossref]  

  9. Dou, J., Bui, D. T., Yunus, A. P., Jia, K., Song, X., Revhaug, I., … Zhu, Z. (2015). Optimization of causative factors for landslide susceptibility evaluation using remote sensing and GIS data in parts of Niigata, Japan. PLoS ONE, 10(7). [https://doi.org/10.1371/journal.pone.0133262">Crossref]

  10. Fell, R., Corominas, J., Bonnard, C., Cascini, L., Leroi, E., & Savage, W. Z. (2008). Guidelines for landslide susceptibility, hazard and risk zoning for land-use planning. Engineering Geology, 102(3–4), 99–111. [https://doi.org/10.1016/j.enggeo.2008.03.014">Crossref]

  11. García-Ruiz, J. M., Beguería, S., Alatorre, L. C., & Puigdefábregas, J. (2010). Land cover changes and shallow landsliding in the flysch sector of the Spanish Pyrenees. Geomorphology, 124(3–4), 250–259. [https://doi.org/10.1016/j.geomorph.2010.03.036">Crossref]

  12. Glade, T. (2003). Landslide occurrence as a response to land use change: A review of evidence from New Zealand. Catena, 51(3–4), 297–314. [https://doi.org/10.1016/S0341-8162(02)00170-4">Crossref]  

  13. Gorsevski, P. V., Gessler, P. E., Boll, J., Elliot, W. J., & Foltz, R. B. (2006). Spatially and temporally distributed modeling of landslide susceptibility. Geomorphology, 80(3–4), 178–198. [https://doi.org/10.1016/j.geomorph.2006.02.011">Crossref]

  14. Hasnawir, Kubota, T., Sanchez-Castillo, L., & Soma, A. S. (2017). The Influence of Land use change and rainfall on shallow landslide in Tanralili Sub-watwrshed, Indonesia. Journal of the Faculty of Agriculture, Kyushu University, 62(1), 171–176.

  15. Hedley, J. D., Roelfsema, C. M., Phinn, S. R., & Mumby, P. J. (2012). Environmental and Sensor Limitations in Optical Remote Sensing of Coral Reefs: Implications for Monitoring and Sensor Design. Remote Sensing, 4(12), 271–302. [https://doi.org/10.3390/rs4010271">Crossref]

  16. Kanungo, D. P., Arora, M. K., Sarkar, S., & Gupta, R. P. (2006). A comparative study of conventional, ANN black box, fuzzy and combined neural and fuzzy weighting procedures for landslide susceptibility zonation in Darjeeling Himalayas. Engineering Geology, 85(3–4), 347–366. [https://doi.org/10.1016/j.enggeo.2006.03.004">Crossref]

  17. Kubota, T., Sanchez-castillo, L., & Soma, A. S. (2015). Root strength of understory vegetation for erosion control on (Micce).

  18. Lang, R., Shao, G., Pijanowski, B. C., & Farnsworth, R. L. (2008). Optimizing unsupervised classifications of remotely sensed imagery with a data-assisted labeling approach. Computers and Geosciences, 34(12), 1877–1885. [https://doi.org/10.1016/j.cageo.2007.10.011">Crossref]  

  19. Lee, S., & Lee, M. J. (2006). Detecting landslide location using KOMPSAT 1 and its application to landslide-susceptibility mapping at the Gangneung area, Korea. Advances in Space Research, 38(10), 2261–2271. [https://doi.org/10.1016/j.asr.2006.03.036">Crossref]  

  20. Meten, M., Prakashbhandary, N., & Yatabe, R. (2015). Effect of Landslide Factor Combinations on the Prediction Accuracy of Landslide Susceptibility Maps in the Blue Nile Gorge of Central Ethiopia. Geoenvironmental Disasters, 2(1), 1–17. [https://doi.org/10.1186/s40677-015-0016-7">Crossref]  

  21. Mugagga, F., Kakembo, V., & Buyinza, M. (2012). Land use changes on the slopes of Mount Elgon and the implications for the occurrence of landslides. Catena, 90, 39–46. [https://doi.org/10.1016/j.catena.2011.11.004">Crossref]

  22. Rasyid, A. R., Bhandary, N. P., & Yatabe, R. (2016). Performance of frequency ratio and logistic regression model in creating GIS based landslides susceptibility map at Lompobattang Mountain, Indonesia. Geoenvironmental Disasters, 3(1), 19. [https://doi.org/10.1186/s40677-016-0053-x">Crossref]  

  23. Shirzadi, A., Saro, L., Hyun Joo, O., & Chapi, K. (2012). A GIS-based logistic regression model in rock-fall susceptibility mapping along a mountainous road: Salavat Abad case study, Kurdistan, Iran. Natural Hazards, 64(2), 1639–1656. [https://doi.org/10.1007/s11069-012-0321-3">Crossref]  

  24. Soma, A. S., & Kubota, T. (2017). Land Use Changes on the Slopes and the Implications for the Landslide Occurrences in Ujung-Loe Watersheds South Sulawesi Indonesia. International Journal of Ecology & Development, 32(2), 33–42.

  25. Xu, C., Xu, X., Dai, F., Wu, Z., He, H., Shi, F., … Xu, S. (2013). Application of an incomplete landslide inventory, logistic regression model and its validation for landslide susceptibility mapping related to the May 12, 2008 Wenchuan earthquake of China. Natural Hazards, 68(2), 883–900. [https://doi.org/10.1007/s11069-013-0661-7">Crossref]


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